Hybrid ZnO–MnO₂ Nanomaterials for Glucose Detection: A Review of Recent Progress - Shaheen, Saira (Biological and Clinical Sciences Research Journal, 2025)
Accurate and timely glucose detection is critical in biomedical diagnostics, particularly for effective management of diabetes mellitus. Traditional glucose sensors often face challenges in sensitivity, selectivity, and stability, necessitating the development of novel materials. Objective: This review aims to critically analyze the recent advances in the development of zinc oxide (ZnO) and manganese dioxide (MnO₂) nanocomposite-based glucose sensors, emphasizing their potential in improving sensor performance. Methods: A comprehensive literature review was conducted focusing on studies published over the past decade. Sources were selected from peer-reviewed journals indexed in databases such as PubMed, Scopus, and Web of Science. Key aspects reviewed include synthesis strategies (e.g., hydrothermal, sol-gel, chemical bath deposition), structural and morphological analysis via techniques such as SEM, TEM, and XRD, and electrochemical evaluation through methods including cyclic voltammetry and chronoamperometry. Comparative performance metrics like sensitivity (µA·mM⁻¹·cm⁻²), detection limit (µM), linear response range, and response time (s) were extracted and analyzed. Results: ZnO-MnO₂ nanocomposites demonstrated notable improvements in glucose sensor performance, offering enhanced electron transfer kinetics, greater surface area for enzyme immobilization, and improved biocompatibility. Sensitivities ranged up to 3670 µA·mM⁻¹·cm⁻², with detection limits as low as 0.3 µM and response times below 5 seconds. Composite formation methods significantly influenced morphology and, consequently, the electrochemical behavior of sensors. Conclusion: ZnO-MnO₂ nanocomposites hold considerable promise as materials for high-performance glucose sensing. Their synergistic electrochemical properties enable superior analytical characteristics. However, challenges remain in terms of long-term stability, reproducibility, and cost-effective scale-up. Future research should focus on integration into wearable platforms, real-time monitoring, and non-enzymatic detection approaches.